其他摘要：In this paper we propose a multiobjective modified differential evolution based feature selection and classifier ensemble approach for biochemical entity extraction. The algorithm performs in two layers. The first layer concerns with determining an appropriate set of features for the task within the framework of a super- vised statistical classifier, namely, Conditional Random Field (CRF). This produces a set of solutions, a subset of which is used to construct an ensemble in the second layer. The proposed approach is evaluated for entity ex- traction in chemical texts, which involves identification of IUPAC and IUPAC-like names and classification of them into some predefined categories. Experiments that were carried out on a benchmark dataset show the recall, precision and F-measure values of 86.15%, 91.29% and 88.64%, respectively.
关键词：Multiobjective modified differential evolution (MODE); feature selection; ensemble learning; condi- tional random field (CRF); named entity (NE);Multiobjective modified differential evolution (MODE); feature selection; ensemble learning; condi- tional random field (CRF); named entity (NE)
